Overview

Dataset statistics

Number of variables22
Number of observations22224
Missing cells4915
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory176.0 B

Variable types

Numeric10
Text5
DateTime3
Categorical4

Alerts

CITY has constant value ""Constant
STATE has constant value ""Constant
ADDRESS NUMBER START is highly overall correlated with ADDRESS NUMBER and 3 other fieldsHigh correlation
ADDRESS NUMBER is highly overall correlated with ADDRESS NUMBER START and 3 other fieldsHigh correlation
WARD is highly overall correlated with POLICE DISTRICT and 1 other fieldsHigh correlation
POLICE DISTRICT is highly overall correlated with ADDRESS NUMBER START and 3 other fieldsHigh correlation
LATITUDE is highly overall correlated with ADDRESS NUMBER START and 4 other fieldsHigh correlation
LONGITUDE is highly overall correlated with ADDRESS NUMBER START and 2 other fieldsHigh correlation
STREET TYPE is highly imbalanced (51.9%)Imbalance
STREET TYPE has 1360 (6.1%) missing valuesMissing
POLICE DISTRICT has 881 (4.0%) missing valuesMissing
LATITUDE has 881 (4.0%) missing valuesMissing
LONGITUDE has 881 (4.0%) missing valuesMissing
LOCATION has 881 (4.0%) missing valuesMissing
PERMIT NUMBER has unique valuesUnique
ADDRESS NUMBER START has 2117 (9.5%) zerosZeros
ADDRESS NUMBER has 2117 (9.5%) zerosZeros

Reproduction

Analysis started2023-11-14 23:04:59.309665
Analysis finished2023-11-14 23:05:25.592951
Duration26.28 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

PERMIT NUMBER
Real number (ℝ)

UNIQUE 

Distinct22224
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1173665.8
Minimum1000571
Maximum1866738
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:25.772336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1000571
5-th percentile1025232.3
Q11078125
median1111338.5
Q31133270.8
95-th percentile1694610
Maximum1866738
Range866167
Interquartile range (IQR)55145.75

Descriptive statistics

Standard deviation204435.56
Coefficient of variation (CV)0.17418549
Kurtosis2.9928554
Mean1173665.8
Median Absolute Deviation (MAD)25491
Skewness2.1118273
Sum2.6083549 × 1010
Variance4.1793899 × 1010
MonotonicityNot monotonic
2023-11-14T23:05:26.057343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1556602 1
 
< 0.1%
1121733 1
 
< 0.1%
1121742 1
 
< 0.1%
1121741 1
 
< 0.1%
1121739 1
 
< 0.1%
1121738 1
 
< 0.1%
1121737 1
 
< 0.1%
1121736 1
 
< 0.1%
1121735 1
 
< 0.1%
1121734 1
 
< 0.1%
Other values (22214) 22214
> 99.9%
ValueCountFrequency (%)
1000571 1
< 0.1%
1001307 1
< 0.1%
1002652 1
< 0.1%
1002993 1
< 0.1%
1003612 1
< 0.1%
1004393 1
< 0.1%
1007248 1
< 0.1%
1007265 1
< 0.1%
1007306 1
< 0.1%
1007406 1
< 0.1%
ValueCountFrequency (%)
1866738 1
< 0.1%
1862206 1
< 0.1%
1860048 1
< 0.1%
1859432 1
< 0.1%
1857612 1
< 0.1%
1855208 1
< 0.1%
1854101 1
< 0.1%
1852797 1
< 0.1%
1852784 1
< 0.1%
1848630 1
< 0.1%

ACCOUNT NUMBER
Real number (ℝ)

Distinct3299
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210928.11
Minimum12
Maximum496618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:26.390367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile5310
Q123392
median261427
Q3352222.25
95-th percentile421498
Maximum496618
Range496606
Interquartile range (IQR)328830.25

Descriptive statistics

Standard deviation157585.62
Coefficient of variation (CV)0.74710579
Kurtosis-1.5819454
Mean210928.11
Median Absolute Deviation (MAD)137671
Skewness-0.11089776
Sum4.6876664 × 109
Variance2.4833226 × 1010
MonotonicityNot monotonic
2023-11-14T23:05:26.677099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63414 956
 
4.3%
65004 320
 
1.4%
298727 114
 
0.5%
50161 87
 
0.4%
230211 66
 
0.3%
22633 66
 
0.3%
369504 65
 
0.3%
17658 56
 
0.3%
392906 50
 
0.2%
267891 48
 
0.2%
Other values (3289) 20396
91.8%
ValueCountFrequency (%)
12 6
 
< 0.1%
13 23
0.1%
16 4
 
< 0.1%
27 4
 
< 0.1%
28 11
< 0.1%
46 18
0.1%
51 6
 
< 0.1%
66 2
 
< 0.1%
67 20
0.1%
73 17
0.1%
ValueCountFrequency (%)
496618 1
< 0.1%
496437 1
< 0.1%
495513 1
< 0.1%
495456 1
< 0.1%
494737 1
< 0.1%
494369 1
< 0.1%
494267 1
< 0.1%
493688 1
< 0.1%
493639 1
< 0.1%
493621 1
< 0.1%

SITE NUMBER
Real number (ℝ)

Distinct105
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.225027
Minimum1
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:27.119391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile21
Maximum230
Range229
Interquartile range (IQR)1

Descriptive statistics

Standard deviation17.949894
Coefficient of variation (CV)3.4353687
Kurtosis44.370416
Mean5.225027
Median Absolute Deviation (MAD)0
Skewness6.189469
Sum116121
Variance322.1987
MonotonicityNot monotonic
2023-11-14T23:05:27.679282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 16344
73.5%
2 2562
 
11.5%
3 661
 
3.0%
4 318
 
1.4%
5 229
 
1.0%
7 124
 
0.6%
6 106
 
0.5%
11 98
 
0.4%
12 96
 
0.4%
19 94
 
0.4%
Other values (95) 1592
 
7.2%
ValueCountFrequency (%)
1 16344
73.5%
2 2562
 
11.5%
3 661
 
3.0%
4 318
 
1.4%
5 229
 
1.0%
6 106
 
0.5%
7 124
 
0.6%
8 65
 
0.3%
9 77
 
0.3%
10 71
 
0.3%
ValueCountFrequency (%)
230 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
224 1
< 0.1%
221 1
< 0.1%
220 1
< 0.1%
218 1
< 0.1%
217 1
< 0.1%
Distinct3312
Distinct (%)14.9%
Missing1
Missing (%)< 0.1%
Memory size173.8 KiB
2023-11-14T23:05:28.190439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length67
Median length46
Mean length21.106061
Min length4

Characters and Unicode

Total characters469040
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique724 ?
Unique (%)3.3%

Sample

1st rowTHE LIFEWAY KEFIR SHOP LLC
2nd rowJERRY'S SANDWICHES LS, LLC
3rd rowETTA RIVER NORTH, LLC
4th rowSQUARE KITCHEN, LLC
5th rowROCCO'S, LLC
ValueCountFrequency (%)
inc 9846
 
12.9%
llc 6784
 
8.9%
corporation 1690
 
2.2%
restaurant 1543
 
2.0%
1390
 
1.8%
starbucks 956
 
1.3%
chicago 935
 
1.2%
corp 928
 
1.2%
the 919
 
1.2%
cafe 756
 
1.0%
Other values (3904) 50335
66.2%
2023-11-14T23:05:29.578698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54030
 
11.5%
C 33251
 
7.1%
I 31975
 
6.8%
A 31511
 
6.7%
N 30771
 
6.6%
L 29178
 
6.2%
O 28211
 
6.0%
R 27806
 
5.9%
E 27661
 
5.9%
T 22942
 
4.9%
Other values (68) 151704
32.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 368739
78.6%
Space Separator 54030
 
11.5%
Other Punctuation 29102
 
6.2%
Lowercase Letter 8832
 
1.9%
Decimal Number 7469
 
1.6%
Dash Punctuation 669
 
0.1%
Open Punctuation 92
 
< 0.1%
Close Punctuation 92
 
< 0.1%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 33251
 
9.0%
I 31975
 
8.7%
A 31511
 
8.5%
N 30771
 
8.3%
L 29178
 
7.9%
O 28211
 
7.7%
R 27806
 
7.5%
E 27661
 
7.5%
T 22942
 
6.2%
S 22261
 
6.0%
Other values (16) 83172
22.6%
Lowercase Letter
ValueCountFrequency (%)
e 1006
11.4%
a 1004
11.4%
n 881
10.0%
o 736
8.3%
t 661
 
7.5%
r 639
 
7.2%
i 630
 
7.1%
s 563
 
6.4%
c 481
 
5.4%
l 420
 
4.8%
Other values (16) 1811
20.5%
Other Punctuation
ValueCountFrequency (%)
. 12834
44.1%
, 12327
42.4%
' 2488
 
8.5%
& 1221
 
4.2%
# 98
 
0.3%
/ 81
 
0.3%
" 38
 
0.1%
@ 7
 
< 0.1%
! 6
 
< 0.1%
: 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1393
18.7%
2 1051
14.1%
0 942
12.6%
5 889
11.9%
3 847
11.3%
4 793
10.6%
8 521
 
7.0%
6 383
 
5.1%
7 369
 
4.9%
9 281
 
3.8%
Space Separator
ValueCountFrequency (%)
54030
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 669
100.0%
Open Punctuation
ValueCountFrequency (%)
( 92
100.0%
Close Punctuation
ValueCountFrequency (%)
) 92
100.0%
Math Symbol
ValueCountFrequency (%)
+ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 377571
80.5%
Common 91469
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 33251
 
8.8%
I 31975
 
8.5%
A 31511
 
8.3%
N 30771
 
8.1%
L 29178
 
7.7%
O 28211
 
7.5%
R 27806
 
7.4%
E 27661
 
7.3%
T 22942
 
6.1%
S 22261
 
5.9%
Other values (42) 92004
24.4%
Common
ValueCountFrequency (%)
54030
59.1%
. 12834
 
14.0%
, 12327
 
13.5%
' 2488
 
2.7%
1 1393
 
1.5%
& 1221
 
1.3%
2 1051
 
1.1%
0 942
 
1.0%
5 889
 
1.0%
3 847
 
0.9%
Other values (16) 3447
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 469040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54030
 
11.5%
C 33251
 
7.1%
I 31975
 
6.8%
A 31511
 
6.7%
N 30771
 
6.6%
L 29178
 
6.2%
O 28211
 
6.0%
R 27806
 
5.9%
E 27661
 
5.9%
T 22942
 
4.9%
Other values (68) 151704
32.3%
Distinct3382
Distinct (%)15.2%
Missing1
Missing (%)< 0.1%
Memory size173.8 KiB
2023-11-14T23:05:30.104413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length88
Median length48
Mean length16.865455
Min length1

Characters and Unicode

Total characters374801
Distinct characters77
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique712 ?
Unique (%)3.2%

Sample

1st rowLIFEWAY KEFIR SHOP
2nd rowJERRY'S SANDWICHES
3rd rowETTA
4th rowFORK
5th rowRANALLI'S
ValueCountFrequency (%)
2640
 
4.3%
cafe 1547
 
2.5%
the 1500
 
2.4%
coffee 1477
 
2.4%
restaurant 1456
 
2.4%
bar 1286
 
2.1%
grill 1143
 
1.9%
starbucks 973
 
1.6%
inc 830
 
1.3%
and 710
 
1.2%
Other values (3762) 48156
78.0%
2023-11-14T23:05:31.053162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39644
 
10.6%
A 31157
 
8.3%
E 28744
 
7.7%
R 22102
 
5.9%
O 21659
 
5.8%
S 21407
 
5.7%
I 19401
 
5.2%
T 19323
 
5.2%
N 18784
 
5.0%
C 16294
 
4.3%
Other values (67) 136286
36.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 296477
79.1%
Space Separator 39644
 
10.6%
Lowercase Letter 21154
 
5.6%
Other Punctuation 10380
 
2.8%
Decimal Number 6579
 
1.8%
Dash Punctuation 513
 
0.1%
Math Symbol 50
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 31157
 
10.5%
E 28744
 
9.7%
R 22102
 
7.5%
O 21659
 
7.3%
S 21407
 
7.2%
I 19401
 
6.5%
T 19323
 
6.5%
N 18784
 
6.3%
C 16294
 
5.5%
L 15905
 
5.4%
Other values (16) 81701
27.6%
Lowercase Letter
ValueCountFrequency (%)
e 2662
12.6%
a 2574
12.2%
o 1714
 
8.1%
n 1683
 
8.0%
i 1560
 
7.4%
r 1504
 
7.1%
t 1274
 
6.0%
s 1236
 
5.8%
l 1232
 
5.8%
u 790
 
3.7%
Other values (15) 4925
23.3%
Other Punctuation
ValueCountFrequency (%)
' 4575
44.1%
& 2036
19.6%
# 1389
 
13.4%
. 990
 
9.5%
, 670
 
6.5%
/ 620
 
6.0%
" 46
 
0.4%
! 43
 
0.4%
@ 8
 
0.1%
; 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1603
24.4%
1 975
14.8%
4 679
10.3%
3 661
10.0%
5 652
9.9%
0 585
 
8.9%
7 433
 
6.6%
6 380
 
5.8%
8 307
 
4.7%
9 304
 
4.6%
Space Separator
ValueCountFrequency (%)
39644
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 513
100.0%
Math Symbol
ValueCountFrequency (%)
+ 50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 317631
84.7%
Common 57170
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 31157
 
9.8%
E 28744
 
9.0%
R 22102
 
7.0%
O 21659
 
6.8%
S 21407
 
6.7%
I 19401
 
6.1%
T 19323
 
6.1%
N 18784
 
5.9%
C 16294
 
5.1%
L 15905
 
5.0%
Other values (41) 102855
32.4%
Common
ValueCountFrequency (%)
39644
69.3%
' 4575
 
8.0%
& 2036
 
3.6%
2 1603
 
2.8%
# 1389
 
2.4%
. 990
 
1.7%
1 975
 
1.7%
4 679
 
1.2%
, 670
 
1.2%
3 661
 
1.2%
Other values (16) 3948
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39644
 
10.6%
A 31157
 
8.3%
E 28744
 
7.7%
R 22102
 
5.9%
O 21659
 
5.8%
S 21407
 
5.7%
I 19401
 
5.2%
T 19323
 
5.2%
N 18784
 
5.0%
C 16294
 
4.3%
Other values (67) 136286
36.4%
Distinct3088
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
Minimum2001-03-14 00:00:00
Maximum2023-10-18 00:00:00
2023-11-14T23:05:31.536812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:32.100224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct29
Distinct (%)0.1%
Missing4
Missing (%)< 0.1%
Memory size173.8 KiB
Minimum2001-11-01 00:00:00
Maximum2027-04-19 00:00:00
2023-11-14T23:05:32.472838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:32.923292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
Distinct3035
Distinct (%)13.7%
Missing22
Missing (%)0.1%
Memory size173.8 KiB
Minimum2001-03-14 00:00:00
Maximum2023-10-18 00:00:00
2023-11-14T23:05:33.361127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:33.766387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2821
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:34.527086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length34
Median length24
Mean length16.555886
Min length10

Characters and Unicode

Total characters367938
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)2.2%

Sample

1st row0 W DIVISION ST
2nd row4739 N LINCOLN AVE
3rd row0 N CLARK ST
4th row4600 N LINCOLN AVE
5th row0 N LINCOLN AVE
ValueCountFrequency (%)
n 10707
 
12.1%
st 10582
 
12.0%
ave 8589
 
9.7%
w 7755
 
8.8%
0 2117
 
2.4%
e 1886
 
2.1%
s 1876
 
2.1%
broadway 1201
 
1.4%
clark 1189
 
1.3%
lincoln 1166
 
1.3%
Other values (2004) 41396
46.8%
2023-11-14T23:05:35.838845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66240
18.0%
A 24441
 
6.6%
N 22760
 
6.2%
E 22613
 
6.1%
S 21611
 
5.9%
T 17421
 
4.7%
W 13312
 
3.6%
1 13018
 
3.5%
L 12990
 
3.5%
R 12803
 
3.5%
Other values (26) 140729
38.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 227823
61.9%
Decimal Number 73875
 
20.1%
Space Separator 66240
 
18.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 24441
10.7%
N 22760
10.0%
E 22613
9.9%
S 21611
 
9.5%
T 17421
 
7.6%
W 13312
 
5.8%
L 12990
 
5.7%
R 12803
 
5.6%
O 12372
 
5.4%
I 11987
 
5.3%
Other values (15) 55513
24.4%
Decimal Number
ValueCountFrequency (%)
1 13018
17.6%
0 11566
15.7%
2 9333
12.6%
3 9260
12.5%
5 8187
11.1%
4 7186
9.7%
6 4626
 
6.3%
7 4201
 
5.7%
8 3433
 
4.6%
9 3065
 
4.1%
Space Separator
ValueCountFrequency (%)
66240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 227823
61.9%
Common 140115
38.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 24441
10.7%
N 22760
10.0%
E 22613
9.9%
S 21611
 
9.5%
T 17421
 
7.6%
W 13312
 
5.8%
L 12990
 
5.7%
R 12803
 
5.6%
O 12372
 
5.4%
I 11987
 
5.3%
Other values (15) 55513
24.4%
Common
ValueCountFrequency (%)
66240
47.3%
1 13018
 
9.3%
0 11566
 
8.3%
2 9333
 
6.7%
3 9260
 
6.6%
5 8187
 
5.8%
4 7186
 
5.1%
6 4626
 
3.3%
7 4201
 
3.0%
8 3433
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 367938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66240
18.0%
A 24441
 
6.6%
N 22760
 
6.2%
E 22613
 
6.1%
S 21611
 
5.9%
T 17421
 
4.7%
W 13312
 
3.6%
1 13018
 
3.5%
L 12990
 
3.5%
R 12803
 
3.5%
Other values (26) 140729
38.2%

ADDRESS NUMBER START
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1758
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1701.5402
Minimum0
Maximum11208
Zeros2117
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:36.195462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1217
median1252
Q32732
95-th percentile5024.85
Maximum11208
Range11208
Interquartile range (IQR)2515

Descriptive statistics

Standard deviation1673.0833
Coefficient of variation (CV)0.98327581
Kurtosis0.91617147
Mean1701.5402
Median Absolute Deviation (MAD)1103
Skewness1.1055635
Sum37815030
Variance2799207.9
MonotonicityNot monotonic
2023-11-14T23:05:36.486928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2117
 
9.5%
200 152
 
0.7%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.4%
20 91
 
0.4%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
100 75
 
0.3%
Other values (1748) 19227
86.5%
ValueCountFrequency (%)
0 2117
9.5%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

ADDRESS NUMBER
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1758
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1701.5402
Minimum0
Maximum11208
Zeros2117
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:36.758627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1217
median1252
Q32732
95-th percentile5024.85
Maximum11208
Range11208
Interquartile range (IQR)2515

Descriptive statistics

Standard deviation1673.0833
Coefficient of variation (CV)0.98327581
Kurtosis0.91617147
Mean1701.5402
Median Absolute Deviation (MAD)1103
Skewness1.1055635
Sum37815030
Variance2799207.9
MonotonicityNot monotonic
2023-11-14T23:05:37.033206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2117
 
9.5%
200 152
 
0.7%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.4%
20 91
 
0.4%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
100 75
 
0.3%
Other values (1748) 19227
86.5%
ValueCountFrequency (%)
0 2117
9.5%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

STREET DIRECTION
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
N
10707 
W
7755 
E
1886 
S
1876 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22224
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 10707
48.2%
W 7755
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

Length

2023-11-14T23:05:37.315405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-14T23:05:37.559273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
n 10707
48.2%
w 7755
34.9%
e 1886
 
8.5%
s 1876
 
8.4%

Most occurring characters

ValueCountFrequency (%)
N 10707
48.2%
W 7755
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22224
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 10707
48.2%
W 7755
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 22224
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 10707
48.2%
W 7755
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 10707
48.2%
W 7755
34.9%
E 1886
 
8.5%
S 1876
 
8.4%

STREET
Text

Distinct233
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:38.081054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length17
Mean length7.025153
Min length3

Characters and Unicode

Total characters156127
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.1%

Sample

1st rowDIVISION
2nd rowLINCOLN
3rd rowCLARK
4th rowLINCOLN
5th rowLINCOLN
ValueCountFrequency (%)
broadway 1201
 
5.2%
clark 1189
 
5.1%
lincoln 1166
 
5.0%
wells 1139
 
4.9%
division 940
 
4.1%
michigan 729
 
3.1%
milwaukee 693
 
3.0%
southport 666
 
2.9%
randolph 658
 
2.8%
state 538
 
2.3%
Other values (235) 14233
61.5%
2023-11-14T23:05:39.210835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 15852
 
10.2%
L 12449
 
8.0%
O 12372
 
7.9%
E 12138
 
7.8%
N 12053
 
7.7%
I 11987
 
7.7%
R 11945
 
7.7%
S 9193
 
5.9%
D 7355
 
4.7%
T 6798
 
4.4%
Other values (25) 43985
28.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 154260
98.8%
Decimal Number 939
 
0.6%
Space Separator 928
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15852
 
10.3%
L 12449
 
8.1%
O 12372
 
8.0%
E 12138
 
7.9%
N 12053
 
7.8%
I 11987
 
7.8%
R 11945
 
7.7%
S 9193
 
6.0%
D 7355
 
4.8%
T 6798
 
4.4%
Other values (15) 42118
27.3%
Decimal Number
ValueCountFrequency (%)
3 312
33.2%
5 228
24.3%
1 147
15.7%
8 60
 
6.4%
6 56
 
6.0%
7 49
 
5.2%
2 48
 
5.1%
9 26
 
2.8%
4 13
 
1.4%
Space Separator
ValueCountFrequency (%)
928
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 154260
98.8%
Common 1867
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15852
 
10.3%
L 12449
 
8.1%
O 12372
 
8.0%
E 12138
 
7.9%
N 12053
 
7.8%
I 11987
 
7.8%
R 11945
 
7.7%
S 9193
 
6.0%
D 7355
 
4.8%
T 6798
 
4.4%
Other values (15) 42118
27.3%
Common
ValueCountFrequency (%)
928
49.7%
3 312
 
16.7%
5 228
 
12.2%
1 147
 
7.9%
8 60
 
3.2%
6 56
 
3.0%
7 49
 
2.6%
2 48
 
2.6%
9 26
 
1.4%
4 13
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15852
 
10.2%
L 12449
 
8.0%
O 12372
 
7.9%
E 12138
 
7.8%
N 12053
 
7.7%
I 11987
 
7.7%
R 11945
 
7.7%
S 9193
 
5.9%
D 7355
 
4.7%
T 6798
 
4.4%
Other values (25) 43985
28.2%

STREET TYPE
Categorical

IMBALANCE  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing1360
Missing (%)6.1%
Memory size173.8 KiB
ST
10542 
AVE
8589 
RD
 
658
BLVD
 
280
PL
 
261
Other values (4)
 
534

Length

Max length4
Median length2
Mean length2.4606499
Min length2

Characters and Unicode

Total characters51339
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST
2nd rowAVE
3rd rowST
4th rowAVE
5th rowAVE

Common Values

ValueCountFrequency (%)
ST 10542
47.4%
AVE 8589
38.6%
RD 658
 
3.0%
BLVD 280
 
1.3%
PL 261
 
1.2%
PKWY 209
 
0.9%
DR 200
 
0.9%
CT 81
 
0.4%
HWY 44
 
0.2%
(Missing) 1360
 
6.1%

Length

2023-11-14T23:05:39.525379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-14T23:05:39.869884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
st 10542
50.5%
ave 8589
41.2%
rd 658
 
3.2%
blvd 280
 
1.3%
pl 261
 
1.3%
pkwy 209
 
1.0%
dr 200
 
1.0%
ct 81
 
0.4%
hwy 44
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 10623
20.7%
S 10542
20.5%
V 8869
17.3%
A 8589
16.7%
E 8589
16.7%
D 1138
 
2.2%
R 858
 
1.7%
L 541
 
1.1%
P 470
 
0.9%
B 280
 
0.5%
Other values (5) 840
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 51339
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 10623
20.7%
S 10542
20.5%
V 8869
17.3%
A 8589
16.7%
E 8589
16.7%
D 1138
 
2.2%
R 858
 
1.7%
L 541
 
1.1%
P 470
 
0.9%
B 280
 
0.5%
Other values (5) 840
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 51339
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 10623
20.7%
S 10542
20.5%
V 8869
17.3%
A 8589
16.7%
E 8589
16.7%
D 1138
 
2.2%
R 858
 
1.7%
L 541
 
1.1%
P 470
 
0.9%
B 280
 
0.5%
Other values (5) 840
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 10623
20.7%
S 10542
20.5%
V 8869
17.3%
A 8589
16.7%
E 8589
16.7%
D 1138
 
2.2%
R 858
 
1.7%
L 541
 
1.1%
P 470
 
0.9%
B 280
 
0.5%
Other values (5) 840
 
1.6%

CITY
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
CHICAGO
22224 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters155568
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHICAGO
2nd rowCHICAGO
3rd rowCHICAGO
4th rowCHICAGO
5th rowCHICAGO

Common Values

ValueCountFrequency (%)
CHICAGO 22224
100.0%

Length

2023-11-14T23:05:40.275050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-14T23:05:40.640848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
chicago 22224
100.0%

Most occurring characters

ValueCountFrequency (%)
C 44448
28.6%
H 22224
14.3%
I 22224
14.3%
A 22224
14.3%
G 22224
14.3%
O 22224
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 155568
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 44448
28.6%
H 22224
14.3%
I 22224
14.3%
A 22224
14.3%
G 22224
14.3%
O 22224
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 155568
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 44448
28.6%
H 22224
14.3%
I 22224
14.3%
A 22224
14.3%
G 22224
14.3%
O 22224
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 44448
28.6%
H 22224
14.3%
I 22224
14.3%
A 22224
14.3%
G 22224
14.3%
O 22224
14.3%

STATE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.8 KiB
IL
22224 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters44448
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIL
2nd rowIL
3rd rowIL
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
IL 22224
100.0%

Length

2023-11-14T23:05:40.889418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-14T23:05:41.110658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
il 22224
100.0%

Most occurring characters

ValueCountFrequency (%)
I 22224
50.0%
L 22224
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 44448
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 22224
50.0%
L 22224
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 22224
50.0%
L 22224
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 22224
50.0%
L 22224
50.0%

ZIP CODE
Real number (ℝ)

Distinct53
Distinct (%)0.2%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean60626.853
Minimum60601
Maximum60707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:41.321670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60601
5-th percentile60603
Q160611
median60618
Q360647
95-th percentile60657
Maximum60707
Range106
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.303677
Coefficient of variation (CV)0.00033489579
Kurtosis-0.95246262
Mean60626.853
Median Absolute Deviation (MAD)11
Skewness0.57200447
Sum1.3471893 × 109
Variance412.23932
MonotonicityNot monotonic
2023-11-14T23:05:41.772041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60657 2234
 
10.1%
60611 1869
 
8.4%
60654 1774
 
8.0%
60622 1758
 
7.9%
60614 1664
 
7.5%
60607 1327
 
6.0%
60613 1170
 
5.3%
60647 967
 
4.4%
60610 951
 
4.3%
60618 908
 
4.1%
Other values (43) 7599
34.2%
ValueCountFrequency (%)
60601 656
3.0%
60602 410
 
1.8%
60603 331
 
1.5%
60604 234
 
1.1%
60605 729
3.3%
60606 385
 
1.7%
60607 1327
6.0%
60608 213
 
1.0%
60609 32
 
0.1%
60610 951
4.3%
ValueCountFrequency (%)
60707 47
 
0.2%
60661 506
 
2.3%
60660 350
 
1.6%
60659 203
 
0.9%
60657 2234
10.1%
60656 17
 
0.1%
60655 3
 
< 0.1%
60654 1774
8.0%
60653 23
 
0.1%
60651 3
 
< 0.1%

WARD
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.356686
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:42.050846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q127
median42
Q344
95-th percentile47
Maximum50
Range49
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.140665
Coefficient of variation (CV)0.49883553
Kurtosis-0.46121055
Mean32.356686
Median Absolute Deviation (MAD)5
Skewness-1.0442388
Sum719095
Variance260.52106
MonotonicityNot monotonic
2023-11-14T23:05:42.340214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
42 5595
25.2%
44 2204
 
9.9%
27 1698
 
7.6%
1 1631
 
7.3%
47 1601
 
7.2%
2 1392
 
6.3%
43 1278
 
5.8%
32 1192
 
5.4%
4 672
 
3.0%
46 624
 
2.8%
Other values (34) 4337
19.5%
ValueCountFrequency (%)
1 1631
7.3%
2 1392
6.3%
3 293
 
1.3%
4 672
3.0%
5 104
 
0.5%
6 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 13
 
0.1%
11 338
 
1.5%
ValueCountFrequency (%)
50 109
 
0.5%
49 164
 
0.7%
48 606
 
2.7%
47 1601
 
7.2%
46 624
 
2.8%
45 270
 
1.2%
44 2204
 
9.9%
43 1278
 
5.8%
42 5595
25.2%
41 91
 
0.4%

POLICE DISTRICT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)0.1%
Missing881
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean14.115963
Minimum0
Maximum25
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:42.617717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median18
Q319
95-th percentile20
Maximum25
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.7903667
Coefficient of variation (CV)0.48104169
Kurtosis-0.23967879
Mean14.115963
Median Absolute Deviation (MAD)1
Skewness-1.0661788
Sum301277
Variance46.10908
MonotonicityNot monotonic
2023-11-14T23:05:42.887613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18 5343
24.0%
19 5064
22.8%
1 3661
16.5%
12 2507
11.3%
14 1789
 
8.0%
20 988
 
4.4%
16 419
 
1.9%
17 374
 
1.7%
24 364
 
1.6%
2 238
 
1.1%
Other values (13) 596
 
2.7%
(Missing) 881
 
4.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3661
16.5%
2 238
 
1.1%
3 10
 
< 0.1%
4 15
 
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 44
 
0.2%
9 224
 
1.0%
ValueCountFrequency (%)
25 149
 
0.7%
24 364
 
1.6%
22 27
 
0.1%
20 988
 
4.4%
19 5064
22.8%
18 5343
24.0%
17 374
 
1.7%
16 419
 
1.9%
15 7
 
< 0.1%
14 1789
 
8.0%

LATITUDE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2727
Distinct (%)12.8%
Missing881
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean41.912361
Minimum41.69067
Maximum42.019421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size173.8 KiB
2023-11-14T23:05:43.178144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum41.69067
5-th percentile41.86652
Q141.885856
median41.903246
Q341.942224
95-th percentile41.978607
Maximum42.019421
Range0.32875146
Interquartile range (IQR)0.056368381

Descriptive statistics

Standard deviation0.038255021
Coefficient of variation (CV)0.00091273839
Kurtosis1.5813341
Mean41.912361
Median Absolute Deviation (MAD)0.021535653
Skewness-0.14545186
Sum894535.52
Variance0.0014634466
MonotonicityNot monotonic
2023-11-14T23:05:43.457295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.88200199 98
 
0.4%
41.88197573 96
 
0.4%
41.90405052 85
 
0.4%
41.87801449 81
 
0.4%
41.88460018 69
 
0.3%
41.8825402 53
 
0.2%
41.90186736 40
 
0.2%
41.89678605 40
 
0.2%
41.88216417 37
 
0.2%
41.87949547 37
 
0.2%
Other values (2717) 20707
93.2%
(Missing) 881
 
4.0%
ValueCountFrequency (%)
41.69066951 1
 
< 0.1%
41.69139989 2
 
< 0.1%
41.69245222 1
 
< 0.1%
41.69920305 10
< 0.1%
41.70289718 1
 
< 0.1%
41.70356373 2
 
< 0.1%
41.71874411 1
 
< 0.1%
41.72107515 10
< 0.1%
41.72112515 1
 
< 0.1%
41.72177014 1
 
< 0.1%
ValueCountFrequency (%)
42.01942097 12
0.1%
42.0193885 5
 
< 0.1%
42.01934594 4
 
< 0.1%
42.01933013 3
 
< 0.1%
42.01932963 2
 
< 0.1%
42.0193098 4
 
< 0.1%
42.01927235 1
 
< 0.1%
42.0174068 8
< 0.1%
42.01615704 15
0.1%
42.01615267 15
0.1%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2727
Distinct (%)12.8%
Missing881
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean-87.65498
Minimum-87.834308
Maximum-87.535139
Zeros0
Zeros (%)0.0%
Negative21343
Negative (%)96.0%
Memory size173.8 KiB
2023-11-14T23:05:43.741922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-87.834308
5-th percentile-87.708499
Q1-87.670876
median-87.648725
Q3-87.630587
95-th percentile-87.624293
Maximum-87.535139
Range0.29916895
Interquartile range (IQR)0.040289762

Descriptive statistics

Standard deviation0.032512663
Coefficient of variation (CV)-0.00037091633
Kurtosis5.619999
Mean-87.65498
Median Absolute Deviation (MAD)0.0196747
Skewness-1.8306663
Sum-1870820.2
Variance0.0010570733
MonotonicityNot monotonic
2023-11-14T23:05:44.229278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.63103164 98
 
0.4%
-87.63397185 96
 
0.4%
-87.62874675 85
 
0.4%
-87.63318903 81
 
0.4%
-87.62798897 69
 
0.3%
-87.62453095 53
 
0.2%
-87.62849214 40
 
0.2%
-87.62828088 40
 
0.2%
-87.62451427 37
 
0.2%
-87.63382966 37
 
0.2%
Other values (2717) 20707
93.2%
(Missing) 881
 
4.0%
ValueCountFrequency (%)
-87.8343079 1
 
< 0.1%
-87.82625503 9
< 0.1%
-87.82618448 1
 
< 0.1%
-87.82167425 5
 
< 0.1%
-87.82042719 4
 
< 0.1%
-87.81865423 16
0.1%
-87.81795264 4
 
< 0.1%
-87.81783297 3
 
< 0.1%
-87.81729036 11
< 0.1%
-87.8172596 2
 
< 0.1%
ValueCountFrequency (%)
-87.53513895 2
 
< 0.1%
-87.55117213 1
 
< 0.1%
-87.55124869 1
 
< 0.1%
-87.55161886 9
< 0.1%
-87.56729719 2
 
< 0.1%
-87.58184369 4
< 0.1%
-87.58390766 1
 
< 0.1%
-87.58502961 2
 
< 0.1%
-87.58781452 8
< 0.1%
-87.58797399 7
< 0.1%

LOCATION
Text

MISSING 

Distinct2727
Distinct (%)12.8%
Missing881
Missing (%)4.0%
Memory size173.8 KiB
2023-11-14T23:05:44.684714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length40
Median length39
Mean length39.102188
Min length35

Characters and Unicode

Total characters834558
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique465 ?
Unique (%)2.2%

Sample

1st row(41.90405051948726, -87.62874675447662)
2nd row(41.96769881732379, -87.68780818484225)
3rd row(41.88200198545344, -87.6310316367502)
4th row(41.964902360748326, -87.68627917084095)
5th row(41.94330292584782, -87.67135515305324)
ValueCountFrequency (%)
41.88200198545344 98
 
0.2%
87.6310316367502 98
 
0.2%
41.881975727713886 96
 
0.2%
87.63397184627037 96
 
0.2%
41.90405051948726 85
 
0.2%
87.62874675447662 85
 
0.2%
41.878014487249544 81
 
0.2%
87.63318903001444 81
 
0.2%
87.62798896732363 69
 
0.2%
41.884600177780484 69
 
0.2%
Other values (5444) 41828
98.0%
2023-11-14T23:05:45.561001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 89104
10.7%
4 80710
9.7%
7 78196
9.4%
6 76501
9.2%
1 74199
8.9%
9 66929
8.0%
2 56939
 
6.8%
3 56179
 
6.7%
5 55900
 
6.7%
0 50500
 
6.1%
Other values (6) 149401
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 685157
82.1%
Other Punctuation 64029
 
7.7%
Open Punctuation 21343
 
2.6%
Space Separator 21343
 
2.6%
Dash Punctuation 21343
 
2.6%
Close Punctuation 21343
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 89104
13.0%
4 80710
11.8%
7 78196
11.4%
6 76501
11.2%
1 74199
10.8%
9 66929
9.8%
2 56939
8.3%
3 56179
8.2%
5 55900
8.2%
0 50500
7.4%
Other Punctuation
ValueCountFrequency (%)
. 42686
66.7%
, 21343
33.3%
Open Punctuation
ValueCountFrequency (%)
( 21343
100.0%
Space Separator
ValueCountFrequency (%)
21343
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21343
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 834558
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 89104
10.7%
4 80710
9.7%
7 78196
9.4%
6 76501
9.2%
1 74199
8.9%
9 66929
8.0%
2 56939
 
6.8%
3 56179
 
6.7%
5 55900
 
6.7%
0 50500
 
6.1%
Other values (6) 149401
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 834558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 89104
10.7%
4 80710
9.7%
7 78196
9.4%
6 76501
9.2%
1 74199
8.9%
9 66929
8.0%
2 56939
 
6.8%
3 56179
 
6.7%
5 55900
 
6.7%
0 50500
 
6.1%
Other values (6) 149401
17.9%

Interactions

2023-11-14T23:05:22.053919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:02.747078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:04.525923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:06.699908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:08.944086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:11.102507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:13.098464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:15.575765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:17.474131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:19.503775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:22.249302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:02.924410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:04.694202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:06.977517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:09.163166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:11.281995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:13.363199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:15.746602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:17.652743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:19.700622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:22.437484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:03.095236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:04.875382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:07.193809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:09.366150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:11.488057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:13.874158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:15.977406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:17.826775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:19.939428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:22.616028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:03.218550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:05.043878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:07.380985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:09.548061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:11.640859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:14.062800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:16.137608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:18.060843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:20.238993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:22.785823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:03.374649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:05.207894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:07.582556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:09.715649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:11.790419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:14.275159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:16.302190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:18.245602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:20.475711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:23.012495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:03.529231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:05.440131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:07.776769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:09.960037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:11.948179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:14.481097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:16.507145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:18.476233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:20.738996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:23.215553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:03.734314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:05.642407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:07.933552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:10.199758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:12.131279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:14.708680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:16.721159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:18.725564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:21.046843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:23.464902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:03.924489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:05.933290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:08.118731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:10.458370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:12.314098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:14.887750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:16.897390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:18.945497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:21.319424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:23.650553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:04.110808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:06.243919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:08.359755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:10.670821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:12.579153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:15.083298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:17.077994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:19.125337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:21.609660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:23.837264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:04.294199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:06.486754image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:08.647468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:10.878451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:12.849600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:15.306833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:17.267274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:19.316309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:05:21.852041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-14T23:05:45.764586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
PERMIT NUMBERACCOUNT NUMBERSITE NUMBERADDRESS NUMBER STARTADDRESS NUMBERZIP CODEWARDPOLICE DISTRICTLATITUDELONGITUDESTREET DIRECTIONSTREET TYPE
PERMIT NUMBER1.0000.4730.010-0.255-0.255-0.003-0.051-0.149-0.1360.0370.0290.002
ACCOUNT NUMBER0.4731.000-0.186-0.141-0.141-0.035-0.087-0.143-0.1240.0160.0710.068
SITE NUMBER0.010-0.1861.000-0.107-0.107-0.0890.060-0.053-0.0720.1420.0690.056
ADDRESS NUMBER START-0.255-0.141-0.1071.0001.0000.3610.2270.5610.734-0.7200.2980.197
ADDRESS NUMBER-0.255-0.141-0.1071.0001.0000.3610.2270.5610.734-0.7200.2980.197
ZIP CODE-0.003-0.035-0.0890.3610.3611.0000.2470.4880.475-0.4440.3140.225
WARD-0.051-0.0870.0600.2270.2270.2471.0000.5630.518-0.0700.2960.167
POLICE DISTRICT-0.149-0.143-0.0530.5610.5610.4880.5631.0000.829-0.3420.3920.186
LATITUDE-0.136-0.124-0.0720.7340.7340.4750.5180.8291.000-0.6210.3920.212
LONGITUDE0.0370.0160.142-0.720-0.720-0.444-0.070-0.342-0.6211.0000.3170.268
STREET DIRECTION0.0290.0710.0690.2980.2980.3140.2960.3920.3920.3171.0000.242
STREET TYPE0.0020.0680.0560.1970.1970.2250.1670.1860.2120.2680.2421.000

Missing values

2023-11-14T23:05:24.150563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-14T23:05:24.762800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-14T23:05:25.249989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PERMIT NUMBERACCOUNT NUMBERSITE NUMBERLEGAL NAMEDOING BUSINESS AS NAMEISSUED DATEEXPIRATION DATEPAYMENT DATEADDRESSADDRESS NUMBER STARTADDRESS NUMBERSTREET DIRECTIONSTREETSTREET TYPECITYSTATEZIP CODEWARDPOLICE DISTRICTLATITUDELONGITUDELOCATION
015566023289921THE LIFEWAY KEFIR SHOP LLCLIFEWAY KEFIR SHOP07/16/202102/28/202207/16/20210 W DIVISION ST00WDIVISIONSTCHICAGOIL60622.0118.041.904051-87.628747(41.90405051948726, -87.62874675447662)
115313033994981JERRY'S SANDWICHES LS, LLCJERRY'S SANDWICHES07/16/202102/28/202207/16/20214739 N LINCOLN AVE47394739NLINCOLNAVECHICAGOIL60625.04719.041.967699-87.687808(41.96769881732379, -87.68780818484225)
215530784631881ETTA RIVER NORTH, LLCETTA07/16/202102/28/202207/16/20210 N CLARK ST00NCLARKSTCHICAGOIL60654.021.041.882002-87.631032(41.88200198545344, -87.6310316367502)
315345562527421SQUARE KITCHEN, LLCFORK07/16/202102/28/202207/16/20214600 N LINCOLN AVE46004600NLINCOLNAVECHICAGOIL60625.04719.041.964902-87.686279(41.964902360748326, -87.68627917084095)
415560063371781ROCCO'S, LLCRANALLI'S07/16/202102/28/202207/16/20210 N LINCOLN AVE00NLINCOLNAVECHICAGOIL60614.043NaNNaNNaNNone
515366214144141BBSC #4 LLCBROWN BAG SEAFOOD CO.07/16/202102/28/202207/16/20213400 N LINCOLN AVE34003400NLINCOLNAVECHICAGOIL60657.04719.041.943303-87.671355(41.94330292584782, -87.67135515305324)
61559950340631GASTHAUS ZUM LOEWEN, INC.THE REVELER07/16/202102/28/202207/16/20210 W ROSCOE ST00WROSCOESTCHICAGOIL60657.032NaNNaNNaNNone
71540360239571TEMPO CAFE LIMITEDTEMPO CAFE07/16/202102/28/202207/15/20216 E CHESTNUT ST66ECHESTNUTSTCHICAGOIL60611.0218.041.898431-87.628009(41.89843137207629, -87.6280091630558)
815433494255401MI FOGATA INC.MI FOGATA INC.07/16/202102/28/202207/16/20214322 N WESTERN AVE43224322NWESTERNAVECHICAGOIL60618.04719.041.960229-87.688800(41.96022917610446, -87.68880023680377)
915551873401261SHINE RESTAURANT CORP.SHINE RESTAURANT, RISE SUSHI RESTAURANT07/17/202102/28/202207/14/20210 W WEBSTER AVE00WWEBSTERAVECHICAGOIL60614.043NaNNaNNaNNone
PERMIT NUMBERACCOUNT NUMBERSITE NUMBERLEGAL NAMEDOING BUSINESS AS NAMEISSUED DATEEXPIRATION DATEPAYMENT DATEADDRESSADDRESS NUMBER STARTADDRESS NUMBERSTREET DIRECTIONSTREETSTREET TYPECITYSTATEZIP CODEWARDPOLICE DISTRICTLATITUDELONGITUDELOCATION
2221418160174902291LA ESQUINA DEL TACO INC.,LA ESQUINA DEL TACO06/21/202302/29/202406/21/20233259 W 63RD ST32593259W63RDSTCHICAGOIL60629.0148.041.778828-87.705434(41.77882757627881, -87.70543426650146)
2221518297074263271MINI MOTT CO.MINI MOTT06/22/202302/29/202406/22/20230 W LOGAN BLVD00WLOGANBLVDCHICAGOIL60647.01NaNNaNNaNNone
2221618272024826042PWU DUMMY ACCOUNTPWU DUMMY ACCOUNT06/22/202302/29/202406/22/20233328 N LINCOLN AVE33283328NLINCOLNAVECHICAGOIL60657.03219.041.942385-87.670714(41.94238547241118, -87.67071383788313)
2221717798152588361WHITE LODGING SERVICES CORPORATIONCOURTYARD BY MARRIOTT06/22/202302/29/202406/22/20230 E ONTARIO ST00EONTARIOSTCHICAGOIL60611.04218.041.893383-87.628054(41.89338314403168, -87.62805370484159)
22218182862075331LA BRUQUENA RESTAURANT & LOUNGE, INC.LA BRUQUENA RESTAURANT & LOUNGE06/23/202302/29/202406/23/20230 W DIVISION ST00WDIVISIONSTCHICAGOIL60622.02618.041.904051-87.628747(41.90405051948726, -87.62874675447662)
22219180237580201MUSES FOOD & LIQUOR, INC.9 MUSES BAR & GRILL06/23/202302/29/202406/23/20230 S HALSTED ST00SHALSTEDSTCHICAGOIL60661.02712.041.881710-87.647504(41.88171010567303, -87.6475041570745)
2222018156702943291RICHMOND TAVERN, INC.RICHMOND TAVERN06/27/202302/29/202406/27/20232944 W GRAND AVE29442944WGRANDAVECHICAGOIL60622.03612.041.896089-87.700748(41.89608917059594, -87.70074823336402)
2222118154874866991CASA AMIGOS RESTAURANT BAR LLCLOS MOLCAJETES RESTAURANT BAR06/27/202302/29/202406/27/20233830 W LAWRENCE AVE38303830WLAWRENCEAVECHICAGOIL60625.03517.041.968390-87.724448(41.968390431264375, -87.72444785924317)
2222218347814631881ETTA RIVER NORTH, LLCETTA06/27/202302/29/202406/27/20230 N CLARK ST00NCLARKSTCHICAGOIL60654.0421.041.882002-87.631032(41.88200198545344, -87.6310316367502)
2222318045364745392PEDESTRIAN COFFEE LLCTHE COFFEE STUDIO06/27/202302/29/202406/27/20235628 N CLARK ST56285628NCLARKSTCHICAGOIL60660.04020.041.984385-87.669099(41.984384824892075, -87.66909931387195)